What is a graphical model?
In this section, we’re going to discuss what graphical causal models (GCMs) are and how they can help in causal inference and discovery.
GCMs can be seen as a useful framework that integrates probabilistic, structural, and graphical aspects of causal inference.
Formally speaking, we can define a graphical causal model as a set consisting of a graph and a set of functions that induce a joint distribution over the variables in the model (Peters et al., 2017).
The basic building blocks of GCM graphs are the same as the basic elements of any directed graph: nodes and directed edges. In a GCM, each node is associated with a variable.
Importantly, in GCMs, edges have a strictly causal interpretation, so that 
means that 
causes 
(this is one of the differentiating factors between causal models and Bayesian networks; Pearl & Mackenzie, 2019, pp. 111-113). GCMs are very powerful because certain combinations of nodes and edges can reveal important...
